Sampling effects on the identification of roadkill hotspots: Implications for survey design

Although locating wildlife roadkill hotspots is essential to mitigate road impacts, the influence of study design on hotspot identification remains uncertain. We evaluated how sampling frequency affects the accuracy of hotspot identification, using a dataset of vertebrate roadkills (n = 4427) recorded over a year of daily surveys along 37 km of roads. "True" hotspots were identified using this baseline dataset, as the 500-m segments where the number of road-killed vertebrates exceeded the upper 95% confidence limit of the mean, assuming a Poisson distribution of road-kills per segment. "Estimated" hotspots were identified likewise, using datasets representing progressively lower sampling frequencies, which were produced by extracting data from the baseline dataset at appropriate time intervals (1-30 days). Overall, 24.3% of segments were "true" hotspots, concentrating 40.4% of roadkills. For different groups, "true" hotspots accounted from 6.8% (bats) to 29.7% (small birds) of road segments, concentrating from <40% (frogs and toads, snakes) to >60% (lizards, lagomorphs, carnivores) of roadkills. Spatial congruence between "true" and "estimated" hotspots declined rapidly with increasing time interval between surveys, due primarily to increasing false negatives (i.e., missing "true" hotspots). There were also false positives (i.e., wrong "estimated" hotspots), particularly at low sampling frequencies. Spatial accuracy decay with increasing time interval between surveys was higher for smaller-bodied (amphibians, reptiles, small birds, small mammals) than for larger-bodied species (birds of prey, hedgehogs, lagomorphs, carnivores). Results suggest that widely used surveys at weekly or longer intervals may produce poor estimates of roadkill hotspots, particularly for small-bodied species. Surveying daily or at two-day intervals may be required to achieve high accuracy in hotspot identification for multiple species.

Ecology of microplastics contamination within food webs of estuarine and coastal ecosystems

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Microplastics contamination of food webs can be approached as part of estuarine ecosystem ecology.

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Standard sampling designs to study estuarine ecology allow comparisons along time and across space.

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The proposed methods detect shifts in fish feeding habit and to demonstrate the diversity of interactions among pollutant, environment and biota.

The aim was to describe a methodology developed to study the relationship among the spatio-temporal patterns of habitat utilization, feeding ecology and microplastics (MPs) contamination across the different ontogenetic phases of fishes belonging to different trophic levels and living along the riverine-estuarine-coastal food web. The Goiana Estuary‘s water column was examined for the seasonal and spatial variation of MPs and their quantification relative to zooplankton, demersal fish species contamination following the same sampling design. The density of MPs in the water column determines their bioavailability. Interest in studies on MPs distribution in relation to spatial and temporal variation of environmental factors and fauna are increasing in quantity and quality. If the ecological strategies presented in this study were replicated in other estuary, comparisons could be made in order to describe how ecosystems work. Standard protocols for sampling, extraction, enumeration and classification of MPs and others pollutant ingested by fishes have been developed and are presented here to encourage comparisons. Standardized and comparable sampling designs and laboratory procedures are an important strategy in order to devise and transfer managerial solutions among different sites and comparisons along time when studying the same environment.

Imprinting statistically sound conclusions for gut microbiota in comparative animal studies: A case study with diet and teleost fishes

Despite the technical progress in high-throughput sequencing technologies, defining the sample size which is capable of yielding representative inferences in metabarcoding analysis still remains debatable. The present study addresses the influence of individual variability in assessing dietary effects on fish gut microbiota parameters and estimates the biological sample size that is sufficient to imprint a statistically secure outcome. European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata) were fed three alternative animal protein diets and a fishmeal control diet. Gut microbiota data from 12 individuals per diet, derived from Illumina sequencing of the V3-V4 region of the 16S rRNA gene, were randomized in all possible combinations of n-1 individuals. Results in this study showcased that increasing the sample size can limit the prevalence of individuals with high microbial load on the outcome and can ensure the statistical confidence required for an accurate validation of dietary-induced microbe shifts. Inter-individual variability was evident in the four dietary treatments where consequently misleading inferences arose from insufficient biological replication. These findings have critical implications for the design of future metabarcoding studies and highlight the urgency in selecting an adequate sample size able to safely elucidate the dietary effects on fish gut microbial communities.

Confidence intervals and sample size for estimating the prevalence of plastic debris in seabird nests

Evidence is accumulating about the impacts of plastics on marine life. The prevalence of plastics in seabird nests has been used as an indicator of levels of this pollutant in the ocean. However, the lack of a framework for defining sample sizes and errors associated with estimating the prevalence of plastic in nests prevents researchers from optimising time and reducing impacts of fieldwork. We present a method to determine the confidence intervals for the prevalence of debris in seabird nests and provide, for the first time, information on the prevalence of these items in nests of the Hartlaub's gull Larus hartlaubii, the African penguin Spheniscus demersus, the great white pelican Pelecanus onocrotalus, and the white-breasted cormorant Phalacrocorax lucidus in South Africa. The method, based on observations and resampling simulations and tested here for nests of 12 seabird species from 15 locations worldwide, allows for straightforward hypothesis testing. Appropriate sample sizes can be defined by combining this method with a Bayesian approach. We show that precise estimates of prevalence of debris in nests can be obtained by sampling around 250 nests. Smaller sample sizes can be useful for obtaining rough estimates. For the Hartlaub's gull, the African penguin, the great white pelican, and the white-breasted cormorant, debris were present in 0.75%, 3.00%, 6.41%, and 25.62% of the respective nests. Our approach will help researchers to determine errors associated with the prevalence of debris recorded in seabird nests and to optimise time and costs spent collecting data. It can also be applied to estimate confidence intervals and define sample sizes for assessing prevalence of plastic ingestion by any organism.

Benthic dinoflagellates: Testing the reliability of the artificial substrate method in the Macaronesian region

The suitability of the 'artificial substrate' method, i.e. standardized surfaces of fiberglass screens, for the quantification of four benthic harmful algal bloom (BHAB) dinoflagellates (Gambierdiscus, Ostreopsis, Prorocentrum and Coolia) was tested relative to estimates from natural macroalgal substrates. Sampling took place in a variety of intertidal and subtidal coastal habitats under different water motion conditions, at depths from 1 to 7 m, in two archipelagos of the Macaronesia region: The Canary Islands and Cape Verde. An immersion time of 24 h was sufficient to adequately estimate dinoflagellate abundances. Seven replicates were established as the ideal replication level, considering both reproducibility and sampling effort. In most cases, cell abundances of the four dinoflagellate genera showed lower variability on artificial substrates than on macroalgae, leading to more reliable estimates of abundances. The ratio of mean cell abundances on artificial substrates to mean cell abundances on macroalgae highly varied among sampling sites for each genus. This was especially true for Ostreopsis and Coolia. Thus, given the potentially harmful nature of benthic dinoflagellates, the transformation of abundances expressed as cells g^-1 of macroalgae to abundances expressed as cells cm^-2 is risky, and it should not be attempted in monitoring and management programs of harmful microalgae. In summary, results of this study support the use of artificial substrates in monitoring programs of BHAB dinoflagellates, while the risks of using macroalgae are stressed.

A two-step semiparametric method to accommodate sampling weights in multiple imputation

Multiple imputation (MI) is a well-established method to handle item-nonresponse in sample surveys. Survey data obtained from complex sampling designs often involve features that include unequal probability of selection. MI requires imputation to be congenial, that is, for the imputations to come from a Bayesian predictive distribution and for the observed and complete data estimator to equal the posterior mean given the observed or complete data, and similarly for the observed and complete variance estimator to equal the posterior variance given the observed or complete data; more colloquially, the analyst and imputer make similar modeling assumptions. Yet multiply imputed data sets from complex sample designs with unequal sampling weights are typically imputed under simple random sampling assumptions and then analyzed using methods that account for the sampling weights. This is a setting in which the analyst assumes more than the imputer, which can led to biased estimates and anti-conservative inference. Less commonly used alternatives such as including case weights as predictors in the imputation model typically require interaction terms for more complex estimators such as regression coefficients, and can be vulnerable to model misspecification and difficult to implement. We develop a simple two-step MI framework that accounts for sampling weights using a weighted finite population Bayesian bootstrap method to validly impute the whole population (including item nonresponse) from the observed data. In the second step, having generated posterior predictive distributions of the entire population, we use standard IID imputation to handle the item nonresponse. Simulation results show that the proposed method has good frequentist properties and is robust to model misspecification compared to alternative approaches. We apply the proposed method to accommodate missing data in the Behavioral Risk Factor Surveillance System when estimating means and parameters of regression models.

Evaluation of optimized bronchoalveolar lavage sampling designs for characterization of pulmonary drug distribution

Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid ≥ LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs.

Spatial Variation of Soil Lead in an Urban Community Garden: Implications for Risk-Based Sampling

Soil lead pollution is a recalcitrant problem in urban areas resulting from a combination of historical residential, industrial, and transportation practices. The emergence of urban gardening movements in postindustrial cities necessitates accurate assessment of soil lead levels to ensure safe gardening. In this study, we examined small-scale spatial variability of soil lead within a 15 × 30 m urban garden plot established on two adjacent residential lots located in Detroit, Michigan, USA. Eighty samples collected using a variably spaced sampling grid were analyzed for total, fine fraction (less than 250 μm), and bioaccessible soil lead. Measured concentrations varied at sampling scales of 1-10 m and a hot spot exceeding 400 ppm total soil lead was identified in the northwest portion of the site. An interpolated map of total lead was treated as an exhaustive data set, and random sampling was simulated to generate Monte Carlo distributions and evaluate alternative sampling strategies intended to estimate the average soil lead concentration or detect hot spots. Increasing the number of individual samples decreases the probability of overlooking the hot spot (type II error). However, the practice of compositing and averaging samples decreased the probability of overestimating the mean concentration (type I error) at the expense of increasing the chance for type II error. The results reported here suggest a need to reconsider U.S. Environmental Protection Agency sampling objectives and consequent guidelines for reclaimed city lots where soil lead distributions are expected to be nonuniform.

Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania

BACKGROUND

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN).

RESULTS

The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.

CONCLUSION

Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.

Perceived multiple stressor effects depend on sample size and stressor gradient length

Multiple stressors are continuously deteriorating surface waters worldwide, posing many challenges for their conservation and restoration. Combined effect types of multiple stressors range from single-stressor dominance to complex interactions. Identifying prevalent combined effect types is critical for environmental management, as it helps to prioritise key stressors for mitigation. However, it remains unclear whether observed single and combined stressor effects reflect true ecological processes unbiased by sample size and length of stressor gradients. Therefore, we examined the role of sample size and stressor gradient lengths in 158 paired-stressor response cases with over 120,000 samples from rivers, lakes, transitional and marine ecosystems around the world. For each case, we split the overall stressor gradient into two partial gradients (lower and upper) and investigated associated changes in single and combined stressor effects. Sample size influenced the identified combined effect types, and stressor interactions were less likely for cases with fewer samples. After splitting gradients, 40 % of cases showed a change in combined effect type, 30 % no change, and 31 % showed a loss in stressor effects. These findings suggest that identified combined effect types may often be statistical artefacts rather than representing ecological processes. In 58 % of cases, we observed changes in stressor effect directions after the gradient split, suggesting unimodal stressor effects. In general, such non-linear responses were more pronounced for organisms at higher trophic levels. We conclude that observed multiple stressor effects are not solely determined by ecological processes, but also strongly depend on sampling design. Observed effects are likely to change when sample size and/or gradient length are modified. Our study highlights the need for improved monitoring programmes with sufficient sample size and stressor gradient coverage. Our findings emphasize the importance of adaptive management, as stress reduction measures or further ecosystem degradation may change multiple stressor-effect relationships, which will then require associated changes in management strategies.

Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.

Optimizing a coastal monitoring network using a water-quality response grid (WRG)-based sampling design for improved reliability and efficiency

Marine monitoring in Bohai Sea is delivered within three networks by lacking appropriate sampling and assessment methodologies. Water-quality response grid (WRG)-based sampling design using optimization and multi-factors assessment can reliably detect a variety of environmental impacts. Which includes 5 steps: selects environmental reference factors, divides the sampling grid, sets the initial stations, optimizes the sampling stations, and assesses the proposed network's reproducibility and efficiency. We applied this method to the Bohai Sea, the networks proposed here have 225 stations for optimized special surveys (OSS) and 181 stations for optimized operational monitoring (OOM), accounting for 46.5% and 37.4% of the original station totals, respectively. Besides, the reproducibility and efficiency index (REI) of OSS and OOM stations approximately 15.4% and 13.3% higher than three current monitoring networks on average among multi-factors in 4 seasons. Thus, the method can improve the reproducibility, efficiency and land-sea spatial matching of monitoring network.

A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits

Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.

When case reporting becomes untenable: Can sewer networks tell us where COVID-19 transmission occurs?

Monitoring SARS-CoV-2 in wastewater is a valuable approach to track COVID-19 transmission. Designing wastewater surveillance (WWS) with representative sampling sites and quantifiable results requires knowledge of the sewerage system and virus fate and transport. We developed a multi-level WWS system to track COVID-19 in Atlanta using an adaptive nested sampling strategy. From March 2021 to April 2022, 868 wastewater samples were collected from influent lines to wastewater treatment facilities and upstream community manholes. Variations in SARS-CoV-2 concentrations in influent line samples preceded similar variations in numbers of reported COVID-19 cases in the corresponding catchment areas. Community sites under nested sampling represented mutually-exclusive catchment areas. Community sites with high SARS-CoV-2 detection rates in wastewater covered high COVID-19 incidence areas, and adaptive sampling enabled identification and tracing of COVID-19 hotspots. This study demonstrates how a well-designed WWS provides actionable information including early warning of surges in cases and identification of disease hotspots.

Coral disease prevalence estimation and sampling design

In the last decades diseases have changed coral communities’ structure and function in reefs worldwide. Studies conducted to evaluate the effect of diseases on corals frequently use modified adaptations of sampling designs that were developed to study ecological aspects of coral reefs. Here we evaluate how efficient these sampling protocols are by generating virtual data for a coral population parameterized with mean coral density and disease prevalence estimates from the Caribbean scleractinian Orbicella faveolata at the Mexican Caribbean. Six scenarios were tested consisting of three patterns of coral colony distribution (random, randomly clustered and randomly over-dispersed) and two disease transmission modes (random and contagious). The virtual populations were sampled with the commonly used method of belt-transects with variable sample-unit sizes (10 × 1, 10 × 2, 25 × 2, 50 × 2 m). Results showed that the probability of obtaining a mean coral disease prevalence estimate of ±5% of the true prevalence value was low (range: 11–48%) and that two-sample comparisons achieved rather low power, unless very large effect sizes existed. Such results imply low statistical confidence to assess differences or changes in coral disease prevalence. The main problem identified was insufficient sample size because local mean colony size, density and spatial distribution of targeted coral species was not taken into consideration to properly adjust the sampling protocols.

Estimation of fukushima radio-cesium deposits by airborne surveys: Sensitivity to the flight-line spacing

After Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, airborne gamma-ray detection was used for regional mapping of soil contamination. For such surveys, the flight-line spacing is an important factor controlling the quality of contamination maps. In this study, cesium-137 (¹³⁷Cs) ground activity is interpolated and mapped using ordinary kriging method; thereafter the error of interpolation is evaluated as a function of flight-line spacing. The analyses were conducted in six 20 km × 20 km test sites with distance of less than 80 km from the FDNPP. In each site, the ordinary kriging estimators were applied to different selections of flight-lines of decreasing density, then punctual and classification errors were calculated. It is demonstrated that these variables are highly correlated (r² > 0.78): increasing the flight-line spacing for 1 km increases the errors from 3% to 9%, depending on the site location. Therefore, flight-line spacing could be designed as a function of acceptable error, determined in the monitoring objectives.

Relation between modern pollen rain, vegetation and climate in northern China: Implications for quantitative vegetation reconstruction in a steppe environment

Vegetation reconstructions from palaeoecological records depend on adequate understanding of relationships between modern pollen, vegetation and climate. A key parameter for quantitative vegetation reconstructions is the Relative Pollen Productivity (RPP). Differences in both environmental and methodological factors are known to alter the RPP estimated significantly, making it difficult to determine whether the underlying pollen productivity does actually vary, and if so, why. In this paper, we present the results of a replication study for the Bashang steppe region, a typical steppe area in northern China, carried out in 2013 and 2014. In each year, 30 surface samples were collected for pollen analysis, with accompanying vegetation survey using the "Crackles Bequest Project" methodology. Sampling designs differed slightly between the two years: in 2013, sites were located completely randomly, whilst in 2014 sampling locations were constrained to be within a few km of roads. There is a strong inter-annual variability in both the pollen and the vegetation spectra therefore in RPPs, and annual precipitation may be a key influence on these variations. The pollen assemblages in both years are dominated by herbaceous taxa such as Artemisia, Amaranthaceae, Poaceae, Asteraceae, Cyperaceae, Fabaceae and Allium. Artemisia and Amaranthaceae pollen are significantly over-represented for their vegetation abundance. Poaceae, Cyperaceae and Fabaceae seem to have under-represented pollen for vegetation with correspondingly lower RPPs. Asteraceae seems to be well-represented, with moderate RPPs and less annual variation. Estimated Relevant Source Area of Pollen (RSAP) ranges from 2000 to 3000m. Different sampling designs have an effect both on RSAP and RPPs and random sample selection may be the best strategy for obtaining robust estimates. Our results have implications for further pollen-vegetation relationship and quantitative vegetation reconstruction research in typical steppe areas and in other open habitats with strong inter-annual variation.

Data quality oriented procedure, for detailed mapping of heavy metals in urban topsoil as an approach to human health risk assessment

Urban soils' health is important to the community because of the soils' potential use for recreational activities. A data quality-oriented approach to sampling design is proposed for performing soil representative surveys that gives support to defensible and statistically-based decisions. Krowoderski park in Cracow (Poland) was selected as a study case to investigate heavy metals (HMs) accumulation and to assess human risk exposure according to simulated scenarios. Statistical power was computed for optimizing the number of samples to compare HMs concentration against legal upper tolerance levels (LUTL). The samples' location was iteratively designed as random spatial distribution throughout the study area, followed by K Ripley's test for selecting the best sampling scheme and avoiding points of clustering or dispersion at several ranges. The total content of Cd, Cu, Pb, Zn, coarse size particles fraction and fine size particles texture, bulk density, pH, total C and S were measured in topsoil at each location using composite sampling. The hydraulic properties were estimated using pedotransfer functions. Statistical analysis of topsoil data shows low correlation between heavy metals, whereas high correlation was found between total S with Cu and Pb as well as total C with Cu and Pb. The concentration of all the HMs analysed was found to be under LUTL in all locations in the park, except for one point that is an outlier for Pb, although the values of several indexes for pooled HMs categorize the park as medium to highly polluted. Spatial autocorrelation was explored for every heavy metal and for elaborated pollution indexes, then maps were drawn using geostatistics. A human health risk assessment (HHRA) was computed for several simulated scenarios finding that risk exists for children from Pb through high ingestion of soil particles.

Temporal variability of suspended solids in construction runoff and evaluation of time-paced sampling strategies

The construction sites have been considered the type of land use with the highest pollution potential, especially due to the erosion of exposed soil surfaces. The runoff monitoring of the construction site was carried out since June 2011 through December 2015. Based on land use land cover (LULC) classification, the monitoring period was divided into active and post-construction phases. Total suspended solids (TSS) showed evident inter-phase variability in average annual event mean concentration (AAEMC) and wash-off pattern. We suggested that stringent runoff control measures should be adopted during active construction phase. Similarly, Personalized Computer Storm Water Management Model (PCSWMM) was applied to evaluate the performance of the time-paced discrete and composite sampling scheme in continuously changing LC scenario. It was found that even though the time-paced composite sampling scheme is more cost effective, it showed lower performance in EMC estimation when compared with the time-paced discrete sampling approach. The results also showed that the storm event monitored at a time discrete frequency of 5 min, 10 min, and 15 min, the maximum expected mean bias will be under the accepted level of 10% of the true EMC value. We concluded that construction phase-specific modifications in sampling scheme provides a view to generate near accurate estimates.

Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration

River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method for quantifying uncertainty in model parameters and model structure, where the latter is usually modelled by an additive or multiplicative stochastic term. Recently, much work has also been done to include input uncertainty in the Bayesian framework. However, the use of geostatistical methods for characterizing the prior distribution of the catchment rainfall is underexplored, particularly in combination with assessments of the influence of increasing or decreasing rain gauge network density on discharge prediction accuracy. In this article we integrate geostatistics and Bayesian calibration to analyze the effect of rain gauge density on river discharge prediction accuracy. We calibrated the HBV hydrological model while accounting for input, initial state, model parameter and model structural uncertainty, and also taking uncertainties in the discharge measurements into account. Results for the Thur basin in Switzerland showed that model parameter uncertainty was the main contributor to the joint posterior uncertainty. We also showed that a low rain gauge density is enough for the Bayesian calibration, and that increasing the number of rain gauges improved model prediction until reaching a density of one gauge per 340 km². While the optimal rain gauge density is case-study specific, we make recommendations on how to handle input uncertainty in Bayesian calibration for river discharge prediction and present the methodology that may be used to carry out such experiments.

Detection of the effects of stormwater control measure in streams using a Bayesian BACI power analysis

The unpredictable timing and magnitude of precipitation events and the spatiotemporal variability of constituent concentrations are major complications to effective monitoring of watershed nutrient and sediment loads. Furthermore, detecting small changes in constituent loads in response to implementation of Stormwater control measures (SCMs) against natural variability is a challenge. Nevertheless, regulatory frameworks that direct reductions of pollutants to streams frequently depend on the ability to quantify changes in loads after management interventions. The before-after-control impact (BACI) sampling design is often used to assess the effects of an environmental change made at a known point in time. However, this approach may be complicated to apply to nutrient and sediment loads in streams as the relative impact of SCMs on nutrient concentration conditional on the long term variability of discharges has not been evaluated. Multi-scale monitoring studies that provide estimates of the natural temporal and spatial variability of discharge and concentrations could provide useful information in designing a BACI study. Here we use data from the Baltimore Long Term Ecological Research (LTER) sites and urban restoration sites to develop multiple statistical measures of the effectiveness of a given monitoring scheme in revealing the hypothesized restoration effects in terms of hydrology and nutrient loads. Stratified sampling over baseflow and stormflow and the use of multiple control streams were useful tools to detect long term cumulative reductions in concentrations due to SCMs. Moderate reductions in concentration (20%), however, were not detectable with the design options considered. We emphasize that appropriate pre-planning of monitoring schemes and sampling frequency is essential to determine if the effects on constituent loads resulting from a given watershed restoration activity are measurable.

Selecting optimal calibration samples using proximal sensing EM induction and γ-ray spectrometry data: An application to managing lime and magnesium in sugarcane growing soil

Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0-0.3 m) Ca (cmol(+) kg^-1) and Mg (cmol(+) kg^-1). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6-0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped.

Testing the efficacy of camera-trap sampling designs for monitoring giant pandas in a heterogeneous landscape

The use of camera traps is prevalent in the ecological study of giant pandas (Ailuropoda melanoleuca). The reliability of camera-trap surveying results greatly depends on sampling designs that significantly influence the detection probability of the target species. Few studies have tested the efficacy of sampling designs on camera-trap surveys for monitoring giant pandas in a heterogeneous landscape. In this study, we conducted camera trapping of giant pandas based on two different sampling schemes in Changqing National Nature Reserve of China, and evaluated their outcomes based on three aspects: occupancy analysis, photographic rate, and activity pattern. The results demonstrated that both climate heterogeneity and distance to the nearest road had a strong positive influence on site occupancy, and slope and forest cover had a significant negative impact on site occupancy. Significant differences in the direction or magnitude of variables' influences indicated that there were apparently spatial-temporal dynamics of giant panda distribution between two sampling schemes. The low detection probabilities indicated that both sampling schemes were not robust to accurately monitor giant pandas in the whole study area. We recommended that more suitable sampling designs with local covariates be developed for camera-trap surveys monitoring giant pandas to account for temporal variability and small-scale variation in heterogeneous landscapes.

Assessment of sediment contamination and sampling design in Savona Harbour, Italy

A method for assessing environmental contamination in harbour sediments and designing the forthcoming monitoring activities in enlarged coastal ecosystems is proposed herein. The method is based on coupling principal component analysis of previous sampling campaigns with a discrete optimisation of a value for money function. The objective function represents the utility derived for every sum of money spent in sampling and chemical analysis. The method was then used to assess actual contamination and found to be well suited for reducing the number of chemicals to be searched during extended monitoring activities and identifying the possible sources of contamination. Data collected in Savona Harbour (Porto Vado), Italy, where construction of a new terminal construction is planned, were used to illustrate the procedure. 23 chemicals were searched for within a total of 213 samples in 68 sampling points during three monitoring campaigns. These data were used to test the procedure. Subsequently, 28 chemicals were searched for within 14 samples in 10 sampling points and collected data were used to evaluate the experimental error and to validate the proposed procedure.

Evaluating food security and nutritional pathways of rural farm families: Empirical evidence from northeast India

The study aimed to ascertain a relationship between agricultural status, socioeconomic factors, and nutrition of farm families. The study was conducted in selected villages in the West Garo Hills district of Meghalaya, using Stratified Random Sampling (St. RS). Using pretested interview schedules, we collected primary data from respondents in 2020 and 2021, focusing on socioeconomic variables, body mass index, and income from agriculture and related sectors. The data was analysed using correlation analyses and separate combined regression estimates for each year and month were obtained. Results from the study indicate that agricultural income significantly influenced nutritional status (p < 0.05) and household income growth was also found significant. The region's agricultural production of cereals, pulses, and vegetables was insufficient, as was the production of meat and meat products, milk, and milk products. Hence, expenditure towards purchasing the above food groups from the market was found to be significant (p < 0.05). Therefore, the markets near the mainland especially in the hilly region play a crucial role in the nutritional pathway of rural farm families.